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Postgresql

Checked
itechmeat·vlatest·database·MIT

Connects to PostgreSQL databases, executes queries, and retrieves data using secure, automated workflows from itechmeat.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add itechmeat/llm-code --skill postgresql -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

itechmeat

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned itechmeat/llm-code at 7ae8a00524.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7ae8a00524
Manifest
skills/postgresql/SKILL.md
Scanned
14 files, 3,411 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The PostgreSQL skill enables AI agents to interact directly with PostgreSQL databases, allowing for schema management, data manipulation, and query execution within a persistent relational environment. It bridges the gap between code generation and database operations by providing direct access to SQL capabilities.

When to use it

  • Deploying backend services that require immediate database initialization and configuration without manual setup.
  • Automating complex data migrations or schema evolution tasks through generated SQL scripts.
  • Prototyping data-driven applications where rapid iteration on table structures is necessary.
  • Executing ad-hoc analytical queries against existing datasets for reporting or debugging purposes.

Key capabilities

  • Schema Management: Create, alter, and drop tables, indexes, and constraints programmatically.
  • Data Manipulation: Insert, update, delete, and select records using standard SQL commands.
  • Connection Handling: Establish secure connections to local or remote PostgreSQL instances.
  • Query Execution: Run complex SELECT statements with joins, aggregations, and filtering logic.

Example prompts

  • "Create a users table with id, email, and created_at fields, then insert three sample records."
  • "Write a SQL query to find all users who signed up in the last 30 days and have not logged in."
  • "Alter the existing products table to add a 'price' column and update existing rows with default values."

Tips & gotchas

Ensure your AI agent has appropriate credentials and network access to reach the target PostgreSQL instance before executing write operations. Always validate generated SQL scripts against production constraints to prevent accidental data loss or schema corruption.